# Simplify-then-Translate: Automatic Preprocessing for Black-Box Machine Translation

arXiv: https://arxiv.org/abs/2005.11197 · HTML: https://mlsanity.com/paper/2005.11197/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 7 of 7)

- [Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension](https://mlsanity.com/paper/2312.10126/) (2023)
- [Improving Automated Program Repair with Domain Adaptation](https://mlsanity.com/paper/2212.11414/) (2022)
- [Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition](https://mlsanity.com/paper/2105.09045/) (2021)
- [Understanding Pre-Editing for Black-Box Neural Machine Translation](https://mlsanity.com/paper/2102.02955/) (2021)
- [Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification](https://mlsanity.com/paper/2104.03848/) (2021)
- [ComplexityMT: Benchmarking the Interaction Between Text Complexity and Machine Translation](https://mlsanity.com/paper/2606.05421/) (2026)
- [Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation](https://mlsanity.com/paper/2606.08011/) (2026)

## Abstract (arXiv)

Black-box machine translation systems have proven incredibly useful for a variety of applications yet by design are hard to adapt, tune to a specific domain, or build on top of. In this work, we introduce a method to improve such systems via automatic pre-processing (APP) using sentence simplification. We first propose a method to automatically generate a large in-domain paraphrase corpus through back-translation with a black-box MT system, which is used to train a paraphrase model that "simplifies" the original sentence to be more conducive for translation. The model is used to preprocess source sentences of multiple low-resource language pairs. We show that this preprocessing leads to better translation performance as compared to non-preprocessed source sentences. We further perform side-by-side human evaluation to verify that translations of the simplified sentences are better than the original ones. Finally, we provide some guidance on recommended language pairs for generating the simplification model corpora by investigating the relationship between ease of translation of a language pair (as measured by BLEU) and quality of the resulting simplification model from back-translations of this language pair (as measured by SARI), and tie this into the downstream task of low-resource translation.